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Non-consensual intimate media (NCIM) involves sharing intimate content without the depicted person's consent, including 'revenge porn' and sexually explicit deepfakes. While NCIM has received attention in legal, psychological, and communication fields over the past decade, it is not sufficiently addressed in computing scholarship. This paper addresses this gap by linking NCIM harms to the specific technological components that facilitate them. We introduce thesociotechnical stack, a conceptual framework designed to map the technical stack to its corresponding social impacts. The sociotechnical stack allows us to analyze sociotechnical problems like NCIM, and points toward opportunities for computing research. We propose a research roadmap for computing and social computing communities to deter NCIM perpetration and support victim-survivors through building and rebuilding technologies. 

Technology plays an increasingly salient role in facilitating intimate partner violence (IPV). Customer support at computer security companies are receiving cases that involve tech-enabled IPV but might not be well equipped to handle these cases. To assess customer support’s existing practices and identify areas for improvement, we conducted five focus groups with professionals who work with IPV survivors (n=17). IPV professionals made numerous suggestions, such as using trauma-informed language, avoiding promises to solve problems, and making referrals to resources and support organizations. To evaluate the practicality of these suggestions, we conducted four focus groups with customer support practitioners (n=11). Support practitioners expressed interest in training agents for IPV cases, but mentioned challenges in identifying potential survivors and frontline agents’ limited capacity to help. We conclude with recommendations for computer security companies to better address tech-enabled IPV through training support agents, tracking the prevalence of these cases, and establishing partnerships with IPV advocates. 

Ballot marking devices (BMDs) allow voters to select candidates on a computer kiosk, which prints a paper ballot that the voter can review before inserting it into a scanner to be tabulated. Unlike paperless voting machines, BMDs provide voters an opportunity to verify an auditable physical record of their choices, and a growing number of U.S. jurisdictions are adopting them for all voters. However, the security of BMDs depends on how reliably voters notice and correct any adversarially induced errors on their printed ballots. In order to measure voters' error detection abilities, we conducted a large study (N = 241) in a realistic polling place setting using real voting machines that we modified to introduce an error into each printout. Without intervention, only 40% of participants reviewed their printed ballots at all, and only 6.6% told a poll worker something was wrong. We also find that carefully designed interventions can improve verification performance. Verbally instructing voters to review the printouts and providing a written slate of candidates for whom to vote both significantly increased review and reporting rates-although the improvements may not be large enough to provide strong security in close elections, especially when BMDs are used by all voters. Based on these findings, we make several evidence-based recommendations to help better defend BMD-based elections. 

Remote censorship measurement tools can now detect DNS- and IP-based blocking at global scale. However, a major unmonitored form of interference is blocking triggered by deep packet inspection of application-layer data. We close this gap by introducing Quack, a scalable, remote measurement system that can efficiently detect application-layer interference. We show that Quack can effectively detect application layer blocking triggered on HTTP and TLS headers, and it is flexible enough to support many other diverse protocols. In experiments, we test for blocking across 4458 autonomous systems, an order of magnitude larger than provided by country probes used by OONI. We also test a corpus of 100,000 keywords from vantage points in 40 countries to produce detailed national blocklists. Finally, we analyze the keywords we find blocked to provide insight into the application-layer blocking ecosystem and compare countries’ behavior. We find that the most consistently blocked services are related to circumvention tools, pornography, and gambling, but that there is significant country-to-country variation. 

We report the first wide-scale measurement study of server-side geographic restriction, or geoblocking, a phenomenon in which server operators intentionally deny access to users from particular countries or regions. Many sites practice geoblocking due to legal requirements or other business reasons, but excessive blocking can needlessly deny valuable content and services to entire national populations. To help researchers and policymakers understand this phenomenon, we develop a semi-automated system to detect instances where whole websites were rendered inaccessible due to geoblocking. By focusing on detecting geoblocking capabilities offered by large CDNs and cloud providers, we can reliably distinguish the practice from dynamic anti-abuse mechanisms and network-based censorship. We apply our techniques to test for geoblocking across the Alexa Top 10K sites from thousands of vantage points in 177 countries. We then expand our measurement to a sample of CDN customers in the Alexa Top 1M. We find that geoblocking occurs across a broad set of countries and sites. We observe geoblocking in nearly all countries we study, with Iran, Syria, Sudan, Cuba, and Russia experiencing the highest rates. These countries experience particularly high rates of geoblocking for finance and banking sites, likely as a result of US economic sanctions. We also verify our measurements with data provided by Cloudflare, and find our observations to be accurate.